1 citations · 1 across the 9 of their papers we have counts for
12 papers
ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning
Seil Kang, Hangoo Kang, Tarun Suresh +4
Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these agents requires long, heterogeneous trajectorie…
Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
Woojung Han, Seil Kang, Youngjun Jun +3
Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws. We reveal a surprising findi…
Real-Time Visual Attribution Streaming in Thinking Model
Seil Kang, Woojung Han, Junhyeok Kim +3
We present an amortized framework for real-time visual attribution streaming in multimodal thinking models. When these models generate code from a screenshot or solve math problems…
ViKey: Enhancing Temporal Understanding in Videos via Visual Prompting
Yeonkyung Lee, Dayun Ju, Youngmin Kim +2
Recent advancements in Video Large Language Models (VideoLLMs) have enabled strong performance across diverse multimodal video tasks. To reduce the high computational cost of proce…
Interpretable Motion-Attentive Maps: Spatio-Temporally Localizing Concepts in Video Diffusion Transformers
Youngjun Jun, Seil Kang, Woojung Han +1
Video Diffusion Transformers (DiTs) have been synthesizing high-quality video with high fidelity from given text descriptions involving motion. However, understanding how Video DiT…
Rare Text Semantics Were Always There in Your Diffusion Transformer
Seil Kang, Woojung Han, Dayun Ju +1
Starting from flow- and diffusion-based transformers, Multi-modal Diffusion Transformers (MM-DiTs) have reshaped text-to-vision generation, gaining acclaim for exceptional visual f…
Interpreting Attention Heads for Image-to-Text Information Flow in Large Vision-Language Models
Jinyeong Kim, Seil Kang, Jiwoo Park +2
Large Vision-Language Models (LVLMs) answer visual questions by transferring information from images to text through a series of attention heads. While this image-to-text informati…
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding
Seil Kang, Jinyeong Kim, Junhyeok Kim +1
Visual grounding seeks to localize the image region corresponding to a free-form text description. Recently, the strong multimodal capabilities of Large Vision-Language Models (LVL…
See What You Are Told: Visual Attention Sink in Large Multimodal Models
Seil Kang, Jinyeong Kim, Junhyeok Kim +1
Large multimodal models (LMMs) "see" images by leveraging the attention mechanism between text and visual tokens in the transformer decoder. Ideally, these models should focus on k…
FALCON: Frequency Adjoint Link with CONtinuous Density Mask for Fast Single Image Dehazing
Donghyun Kim, Seil Kang, Seong Jae Hwang
Image dehazing, addressing atmospheric interference like fog and haze, remains a pervasive challenge crucial for robust vision applications such as surveillance and remote sensing…
WoLF: Wide-scope Large Language Model Framework for CXR Understanding
Seil Kang, Donghyun Kim, Junhyeok Kim +2
Significant methodological strides have been made toward Chest X-ray (CXR) understanding via modern vision-language models (VLMs), demonstrating impressive Visual Question Answerin…
CoBra: Complementary Branch Fusing Class and Semantic Knowledge for Robust Weakly Supervised Semantic Segmentation
Woojung Han, Seil Kang, Kyobin Choo +1
Leveraging semantically precise pseudo masks derived from image-level class knowledge for segmentation, namely image-level Weakly Supervised Semantic Segmentation (WSSS), still rem…